• Optimizing wastewater treatmen...

Treffer: Optimizing wastewater treatment through combined deep learning and deep reinforcement learning: Recent advances and future prospects.

Title:
Optimizing wastewater treatment through combined deep learning and deep reinforcement learning: Recent advances and future prospects.
Authors:
Bai Y; State Key Laboratory of Water Pollution Control and Green Resource Recycling, Shanghai Institute of Pollution Control and Ecological Security, College of Environmental Science and Engineering, Tongji University, Siping Road, Shanghai, 200092, China., Li Z; State Key Laboratory of Water Pollution Control and Green Resource Recycling, Shanghai Institute of Pollution Control and Ecological Security, College of Environmental Science and Engineering, Tongji University, Siping Road, Shanghai, 200092, China., Jiang J; Hubei Key Laboratory of Multi-media Pollution Cooperative Control in Yangtze Basin, School of Environmental Science & Engineering, Huazhong University of Science and Technology (HUST), 1037 Luoyu Road, Wuhan, Hubei, 430074, China., Liu J; Hubei Key Laboratory of Multi-media Pollution Cooperative Control in Yangtze Basin, School of Environmental Science & Engineering, Huazhong University of Science and Technology (HUST), 1037 Luoyu Road, Wuhan, Hubei, 430074, China., Wang H; State Key Laboratory of Water Pollution Control and Green Resource Recycling, Shanghai Institute of Pollution Control and Ecological Security, College of Environmental Science and Engineering, Tongji University, Siping Road, Shanghai, 200092, China. Electronic address: hanw@tongji.edu.cn., Liu Q; State Key Laboratory of Water Pollution Control and Green Resource Recycling, Shanghai Institute of Pollution Control and Ecological Security, College of Environmental Science and Engineering, Tongji University, Siping Road, Shanghai, 200092, China., Guo G; Hubei Key Laboratory of Multi-media Pollution Cooperative Control in Yangtze Basin, School of Environmental Science & Engineering, Huazhong University of Science and Technology (HUST), 1037 Luoyu Road, Wuhan, Hubei, 430074, China., Kong Z; State Key Laboratory of Water Pollution Control and Green Resource Recycling, Shanghai Institute of Pollution Control and Ecological Security, College of Environmental Science and Engineering, Tongji University, Siping Road, Shanghai, 200092, China., Wang Y; State Key Laboratory of Water Pollution Control and Green Resource Recycling, Shanghai Institute of Pollution Control and Ecological Security, College of Environmental Science and Engineering, Tongji University, Siping Road, Shanghai, 200092, China. Electronic address: yayi.wang@tongji.edu.cn.
Source:
Environmental research [Environ Res] 2026 Mar 15; Vol. 293, pp. 123795. Date of Electronic Publication: 2026 Jan 15.
Publication Type:
Journal Article; Review
Language:
English
Journal Info:
Publisher: Elsevier Country of Publication: Netherlands NLM ID: 0147621 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1096-0953 (Electronic) Linking ISSN: 00139351 NLM ISO Abbreviation: Environ Res Subsets: MEDLINE
Imprint Name(s):
Publication: <2000- > : Amsterdam : Elsevier
Original Publication: New York, Academic Press.
MeSH Terms:
Waste Disposal, Fluid*/methods , Water Purification*/methods , Deep Learning* , Wastewater*, Reinforcement Machine Learning
Contributed Indexing:
Keywords: Coupled systems; Data platform; Deep learning; Deep reinforcement learning; Nonlinear data; Process optimization; Wastewater treatment
Substance Nomenclature:
0 (Wastewater)
Entry Date(s):
Date Created: 20260117 Date Completed: 20260207 Latest Revision: 20260207
Update Code:
20260208
DOI:
10.1016/j.envres.2026.123795
PMID:
41547426
Database:
MEDLINE

Weitere Informationen

Wastewater treatment plants (WWTPs) are critical components of urban infrastructure, and enhancing their performance while reducing carbon emissions is essential for advancing sustainable urban development. However, WWTPs often face challenges such as fluctuations in water quality and quantity, limitations in real-time monitoring, and delays in operational adjustments, leading to a failure of meeting the discharge standards. The application of artificial intelligence (AI), particularly deep learning (DL) and deep reinforcement learning (DRL), offers significant potential to resolve these complex issues through innovative process optimization. DL has a strong feature extraction capability and data-driven learning paradigm, and can achieve more accurate fitting than DRL when handling tasks involving nonlinear and highly fluctuating process variables; it is particularly effective in fault detection, water quality prediction, and real-time monitoring in wastewater treatment systems. DRL possess a trial-and-error-based learning paradigm, and exhibits greater potential in decision-oriented applications, such as adaptive control of wastewater treatment processes and multi-objective optimization of wastewater treatment plant operations. By clarifying the underlying computational principles of DL and DRL, this review discusses their application suitability and advantages in the wastewater treatment. It emphasizes the need for standardized and open data platforms to support intelligent coupled systems in dynamic and complex wastewater treatment scenarios, and calls for open-source model repositories and the integration of model transparency approaches. Especially, the practical application of intelligent systems in WWTP operation remains challenging, highlighting the need for reliable and standardized data acquisition for real-world applications.
(Copyright © 2026 Elsevier Inc. All rights reserved.)

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.